Survey of Text Visualization Techniques

Puretskiy, Andrey A.; Shutt, Gregory; Berry, Michael W.

doi:10.1002/9780470689646.ch6

Cited by 7 publications

(8 citation statements)

References 4 publications

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“…Content analysis was conducted by way of key words counts, while the identification of themes was determined by expert analysis and operationalized by way of word cloud generation (Puretskiy et al, 2010).…”

Section: Discussionmentioning

confidence: 99%

The Crisis in Crimea – “Voices” From Canada: A Qualitative Study

McKenzie

2014

JEECAR

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With the movement of Russian troops into the Crimean peninsula, and the subsequent vote on secession from Ukraine and joining with Russia, many voices wanted to be heard. The focus of this study is to provide insight as to the views from an interested, but arguably neutral player in the discussion; the second largest country in the world, but one with the third largest population of those with Ukrainian heritage outside of Ukraine and Russia, Canada. Newspaper articles from the period of the crisis from February and March 2014 were collected and analyzed. The articles were published in three national newspapers and also three newspapers with a significant population of those with Ukrainian heritage. Evidence from this sample of suggests that there was a dominance of negative coverage as to the role of Russia consistent with prior research. The articles reviewed were found to present non-neutral coverage particularly through opinion pieces, which also tended to be both longer and more frequently published than neutral, or alternative articles.

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Section: Discussionmentioning

confidence: 99%

The Crisis in Crimea – “Voices” From Canada: A Qualitative Study

McKenzie

2014

JEECAR

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“…Thus, disadvantages pertaining to text mining are perceivable. For instance, the further interpretation of Text Mining results is necessary, but the reduction of a huge quantity of data is often still much too large for a human analyst [19]. With regard to web pages, they "could be in any language, which complicates an already challenging text mining problem" [19] [20].…”

Section: B Applications Of Text Miningmentioning

confidence: 99%

Benchmarking of Scientific Research Clusters by Use of Text Mining Algorithms on Textual Artefacts

Schroeder

Tummel

Isenhardt

et al. 2016

2016 International Conference on Information Systems Engineering (ICISE)

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The development of text mining algorithms is far reaching and thus, new application areas arise, inter alia in the framework of applying modern information technologies (e.g. big data analytics) in order to answer social and economic (research) questions (digital humanities). In this paper existing algorithms to reveal results in the context of benchmarking scientific research clusters are combined. For conducting the analysis, two data corpora are gathered. The first data corpus contains abstracts of publications according to the used application case of the Cluster of Excellence Tailor-Made Fuels from Biomass 'TMFB' at RWTH Aachen University. The second data corpus -based on the analysis of the first data corpus -contains content-related abstracts of research belonging to the relevant scientific community. In the framework of processing the gathered data, information extraction, information retrieval and named entity recognition among others are utilized for analyzing the publications in order to derive useful findings. Furthermore association mining findings are introduced. As an excerpt of the results, the identification of thematic alignments and developments over time are presented for both corpora and are subsequently compared.

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“…This often requires a great deal of time and effort on the part of human analysts. Visual postprocessing tools tailored for specific TM packages can therefore greatly facilitate the analysis process (Puretskiy, Shutt and Berry, 2010).…”

Section: Clusteringmentioning

confidence: 99%

Analyzing unstructured Facebook social network data through web text mining

Kahya-Özyirmidokuz

2014

Information Development

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The large amounts of Facebook social network data which are generated and collected need to be analyzed for valuable decision making information about shopping firms in Turkey. In addition, analyzing social network data from outside the firms becomes a critical business need for the firms which actively use Facebook. To have a competitive advantage, firms must translate social media texts into something more quantitative to extract information. In this study, web text mining techniques are used to determine popular online shopping firms’ Facebook patterns. For this purpose, 200 popular Turkish companies’ web URLs are used. Web text mining through natural language processing techniques is examined. Similarity analysis and clustering are done. Consequently, the clusters of the Facebook websites and their relationships and similarities of the firms are obtained.

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Survey of Text Visualization Techniques

Cited by 7 publications

References 4 publications

The Crisis in Crimea – “Voices” From Canada: A Qualitative Study

The Crisis in Crimea – “Voices” From Canada: A Qualitative Study

Benchmarking of Scientific Research Clusters by Use of Text Mining Algorithms on Textual Artefacts

Analyzing unstructured Facebook social network data through web text mining

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